Importing the important liberary

import numpy as np
from matplotlib import pyplot as plt 
import csv

Reading the data from table

#User define function to convert csv to dict.
def csv_to_dict(file_path):
    data_dict = {}

    with open(file_path, mode='r') as file:
        # Create a CSV reader
        reader = csv.DictReader(file)

        # Iterate through rows and populate the dictionary
        for row in reader:
            for key, value in row.items():
                if key not in data_dict:
                    data_dict[key] = []
                data_dict[key].append(value)

    return data_dict

#Reading File
csv_file_path = 'homesensors_2021.csv'
df = csv_to_dict(csv_file_path)

# Print the resulting dictionary
print(df)

{'pitime': ['2021-01-02 00:00:00', '2021-01-02 00:30:00', '2021-01-02 01:00:00', '2021-01-02 01:30:00', '2021-01-02 02:00:00', '2021-01-02 02:30:00', '2021-01-02 03:00:00', '2021-01-02 03:30:00', '2021-01-02 04:00:00', '2021-01-02 04:30:00', '2021-01-02 05:00:00', '2021-01-02 05:30:00', '2021-01-02 06:00:00', '2021-01-02 06:30:00', '2021-01-02 07:00:00', '2021-01-02 07:30:00', '2021-01-02 08:00:00', '2021-01-02 08:30:00', '2021-01-02 09:00:00', '2021-01-02 09:30:00', '2021-01-02 10:00:00', '2021-01-02 10:30:00', '2021-01-02 11:00:00', '2021-01-02 11:30:00', '2021-01-02 12:00:00', '2021-01-02 12:30:00', '2021-01-02 13:00:00', '2021-01-02 13:30:00', '2021-01-02 14:00:00', '2021-01-02 14:30:00', '2021-01-02 15:00:00', '2021-01-02 15:30:00', '2021-01-02 16:00:00', '2021-01-02 16:30:00', '2021-01-02 17:00:00', '2021-01-02 17:30:00', '2021-01-02 18:00:00', '2021-01-02 18:30:00', '2021-01-02 19:00:00', '2021-01-02 19:30:00', '2021-01-02 20:00:00', '2021-01-02 20:30:00', '2021-01-02 21:00:00', '2021-01-02 21:30:00', '2021-01-02 22:00:00', '2021-01-02 22:30:00', '2021-01-02 23:00:00', '2021-01-02 23:30:00', '2021-01-03 00:00:00', '2021-01-03 00:30:00', '2021-01-03 01:00:00', '2021-01-03 01:30:00', '2021-01-03 02:00:00', '2021-01-03 02:30:00', '2021-01-03 03:00:00', '2021-01-03 03:30:00', '2021-01-03 04:00:00', '2021-01-03 04:30:00', '2021-01-03 05:00:00', '2021-01-03 05:30:00', '2021-01-03 06:00:00', '2021-01-03 06:30:00', '2021-01-03 07:00:00', '2021-01-03 07:30:00', '2021-01-03 08:00:00', '2021-01-03 08:30:00', '2021-01-03 09:00:00', '2021-01-03 09:30:00', '2021-01-03 10:00:00', '2021-01-03 10:30:00', '2021-01-03 11:00:00', '2021-01-03 11:30:00', '2021-01-03 12:00:00', '2021-01-03 12:30:00', '2021-01-03 13:00:00', '2021-01-03 13:30:00', '2021-01-03 14:00:00', '2021-01-03 14:30:00', '2021-01-03 15:00:00', '2021-01-03 15:30:00', '2021-01-03 16:00:00', '2021-01-03 16:30:00', '2021-01-03 17:00:00', '2021-01-03 17:30:00', '2021-01-03 18:00:00', '2021-01-03 18:30:00', '2021-01-03 19:00:00', '2021-01-03 19:30:00', '2021-01-03 20:00:00', '2021-01-03 20:30:00', '2021-01-03 21:00:00', '2021-01-03 21:30:00', '2021-01-03 22:00:00', '2021-01-03 22:30:00', '2021-01-03 23:00:00', '2021-01-03 23:30:00', '2021-01-04 00:00:00', '2021-01-04 00:30:00', '2021-01-04 01:00:00', '2021-01-04 01:30:00', '2021-01-04 02:00:00', '2021-01-04 02:30:00', '2021-01-04 03:00:00', '2021-01-04 03:30:00', '2021-01-04 04:00:00', '2021-01-04 04:30:00', '2021-01-04 05:00:00', '2021-01-04 05:30:00', '2021-01-04 06:00:00', '2021-01-04 06:30:00', '2021-01-04 07:00:00', '2021-01-04 07:30:00', '2021-01-04 08:00:00', '2021-01-04 08:30:00', '2021-01-04 09:00:00', '2021-01-04 09:30:00', '2021-01-04 10:00:00', '2021-01-04 10:30:00', '2021-01-04 11:00:00', '2021-01-04 11:30:00', '2021-01-04 12:00:00', '2021-01-04 12:30:00', '2021-01-04 13:00:00', '2021-01-04 13:30:00', '2021-01-04 14:00:00', '2021-01-04 14:30:00', '2021-01-04 15:00:00', '2021-01-04 15:30:00', '2021-01-04 16:00:00', '2021-01-04 16:30:00', '2021-01-04 17:00:00', '2021-01-04 17:30:00', '2021-01-04 18:00:00', '2021-01-04 18:30:00', '2021-01-04 19:00:00', '2021-01-04 19:30:00', '2021-01-04 20:00:00', '2021-01-04 20:30:00', '2021-01-04 21:00:00', '2021-01-04 21:30:00', '2021-01-04 22:00:00', '2021-01-04 22:30:00', '2021-01-04 23:00:00', '2021-01-04 23:30:00', '2021-01-05 00:00:00', '2021-01-05 00:30:00', '2021-01-05 01:00:00', '2021-01-05 01:30:00', '2021-01-05 02:00:00', '2021-01-05 02:30:00', '2021-01-05 03:00:00', '2021-01-05 03:30:00', '2021-01-05 04:00:00', '2021-01-05 04:30:00', '2021-01-05 05:00:00', '2021-01-05 05:30:00', '2021-01-05 06:00:00', '2021-01-05 06:30:00', '2021-01-05 07:00:00', '2021-01-05 07:30:00', '2021-01-05 08:00:00', '2021-01-05 08:30:00', '2021-01-05 09:00:00', '2021-01-05 09:30:00', '2021-01-05 10:00:00', '2021-01-05 10:30:00', '2021-01-05 11:00:00', '2021-01-05 11:30:00', '2021-01-05 12:00:00', '2021-01-05 12:30:00', '2021-01-05 13:00:00', '2021-01-05 13:30:00', '2021-01-05 14:00:00', '2021-01-05 14:30:00', '2021-01-05 15:00:00', '2021-01-05 15:30:00', '2021-01-05 16:00:00', '2021-01-05 16:30:00', '2021-01-05 17:00:00', '2021-01-05 17:30:00', '2021-01-05 18:00:00', '2021-01-05 18:30:00', '2021-01-05 19:00:00', '2021-01-05 19:30:00', '2021-01-05 20:00:00', '2021-01-05 20:30:00', '2021-01-05 21:00:00', '2021-01-05 21:30:00', '2021-01-05 22:00:00', '2021-01-05 22:30:00', '2021-01-05 23:00:00', '2021-01-05 23:30:00', '2021-01-06 00:00:00', '2021-01-06 00:30:00', '2021-01-06 01:00:00', '2021-01-06 01:30:00', '2021-01-06 02:00:00', '2021-01-06 02:30:00', '2021-01-06 03:00:00', '2021-01-06 03:30:00', '2021-01-06 04:00:00', '2021-01-06 04:30:00', '2021-01-06 05:00:00', '2021-01-06 05:30:00', '2021-01-06 06:00:00', '2021-01-06 06:30:00', '2021-01-06 07:00:00', '2021-01-06 07:30:00', '2021-01-06 08:00:00', '2021-01-06 08:30:00', '2021-01-06 09:00:00', '2021-01-06 09:30:00', '2021-01-06 10:00:00', '2021-01-06 10:30:00', '2021-01-06 11:00:00', '2021-01-06 11:30:00', '2021-01-06 12:00:00', '2021-01-06 12:30:00', '2021-01-06 13:00:00', '2021-01-06 13:30:00', '2021-01-06 14:00:00', '2021-01-06 14:30:00', '2021-01-06 15:00:00', '2021-01-06 15:30:00', '2021-01-06 16:00:00', '2021-01-06 16:30:00', '2021-01-06 17:00:00', '2021-01-06 17:30:00', '2021-01-06 18:00:00', '2021-01-06 18:30:00', '2021-01-06 19:00:00', '2021-01-06 19:30:00', '2021-01-06 20:00:00', '2021-01-06 20:30:00', '2021-01-06 21:00:00', '2021-01-06 21:30:00', '2021-01-06 22:00:00', '2021-01-06 22:30:00', '2021-01-06 23:00:00', '2021-01-06 23:30:00', '2021-01-07 00:00:00', '2021-01-07 00:30:00', '2021-01-07 01:00:00', '2021-01-07 01:30:00', '2021-01-07 02:00:00', '2021-01-07 02:30:00', '2021-01-07 03:00:00', '2021-01-07 03:30:00', '2021-01-07 04:00:00', '2021-01-07 04:30:00', '2021-01-07 05:00:00', '2021-01-07 05:30:00', '2021-01-07 06:00:00', '2021-01-07 06:30:00', '2021-01-07 07:00:00', '2021-01-07 07:30:00', '2021-01-07 08:00:00', '2021-01-07 08:30:00', '2021-01-07 09:00:00', '2021-01-07 09:30:00', '2021-01-07 10:00:00', '2021-01-07 10:30:00', '2021-01-07 11:00:00', '2021-01-07 11:30:00', '2021-01-07 12:00:00', '2021-01-07 12:30:00', '2021-01-07 13:00:00', '2021-01-07 13:30:00', '2021-01-07 14:00:00', '2021-01-07 14:30:00', '2021-01-07 15:00:00', '2021-01-07 15:30:00', '2021-01-07 16:00:00', '2021-01-07 16:30:00', '2021-01-07 17:00:00', '2021-01-07 17:30:00', '2021-01-07 18:00:00', '2021-01-07 18:30:00', '2021-01-07 19:00:00', '2021-01-07 19:30:00', '2021-01-07 20:00:00', '2021-01-07 20:30:00', '2021-01-07 21:00:00', '2021-01-07 21:30:00', '2021-01-07 22:00:00', '2021-01-07 22:30:00', '2021-01-07 23:00:00', '2021-01-07 23:30:00', '2021-01-08 00:00:00', '2021-01-08 00:30:00', '2021-01-08 01:00:00', '2021-01-08 01:30:00', '2021-01-08 02:00:00', '2021-01-08 02:30:00', '2021-01-08 03:00:00', '2021-01-08 03:30:00', '2021-01-08 04:00:00', '2021-01-08 04:30:00', '2021-01-08 05:00:00', '2021-01-08 05:30:00', '2021-01-08 06:00:00', '2021-01-08 06:30:00', '2021-01-08 07:00:00', '2021-01-08 07:30:00', '2021-01-08 08:00:00', '2021-01-08 08:30:00', '2021-01-08 09:00:00', '2021-01-08 09:30:00', '2021-01-08 10:00:00', '2021-01-08 10:30:00', '2021-01-08 11:00:00', '2021-01-08 11:30:00', '2021-01-08 12:00:00', '2021-01-08 12:30:00', '2021-01-08 13:00:00', '2021-01-08 13:30:00', '2021-01-08 14:00:00', '2021-01-08 14:30:00', '2021-01-08 15:00:00', '2021-01-08 15:30:00', '2021-01-08 16:00:00', '2021-01-08 16:30:00', '2021-01-08 17:00:00', '2021-01-08 17:30:00', '2021-01-08 18:00:00', '2021-01-08 18:30:00', '2021-01-08 19:00:00', '2021-01-08 19:30:00', '2021-01-08 20:00:00', '2021-01-08 20:30:00', '2021-01-08 21:00:00', '2021-01-08 21:30:00', '2021-01-08 22:00:00', '2021-01-08 22:30:00', '2021-01-08 23:00:00', '2021-01-08 23:30:00', '2021-01-09 00:00:00', '2021-01-09 00:30:00', '2021-01-09 01:00:00', '2021-01-09 01:30:00', '2021-01-09 02:00:00', '2021-01-09 02:30:00', '2021-01-09 03:00:00', '2021-01-09 03:30:00', '2021-01-09 04:00:00', '2021-01-09 04:30:00', '2021-01-09 05:00:00', '2021-01-09 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'2021-01-10 03:30:00', '2021-01-10 04:00:00', '2021-01-10 04:30:00', '2021-01-10 05:00:00', '2021-01-10 05:30:00', '2021-01-10 06:00:00', '2021-01-10 06:30:00', '2021-01-10 07:00:00', '2021-01-10 07:30:00', '2021-01-10 08:00:00', '2021-01-10 08:30:00', '2021-01-10 09:00:00', '2021-01-10 09:30:00', '2021-01-10 10:00:00', '2021-01-10 10:30:00', '2021-01-10 11:00:00', '2021-01-10 11:30:00', '2021-01-10 12:00:00', '2021-01-10 12:30:00', '2021-01-10 13:00:00', '2021-01-10 13:30:00', '2021-01-10 14:00:00', '2021-01-10 14:30:00', '2021-01-10 15:00:00', '2021-01-10 15:30:00', '2021-01-10 16:00:00', '2021-01-10 16:30:00', '2021-01-10 17:00:00', '2021-01-10 17:30:00', '2021-01-10 18:00:00', '2021-01-10 18:30:00', '2021-01-10 19:00:00', '2021-01-10 19:30:00', '2021-01-10 20:00:00', '2021-01-10 20:30:00', '2021-01-10 21:00:00', '2021-01-10 21:30:00', '2021-01-10 22:00:00', '2021-01-10 22:30:00', '2021-01-10 23:00:00', '2021-01-10 23:30:00', '2021-01-11 00:00:00', '2021-01-11 00:30:00', '2021-01-11 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'2021-01-11 23:00:00', '2021-01-11 23:30:00', '2021-01-12 00:00:00', '2021-01-12 00:30:00', '2021-01-12 01:00:00', '2021-01-12 01:30:00', '2021-01-12 02:00:00', '2021-01-12 02:30:00', '2021-01-12 03:00:00', '2021-01-12 03:30:00', '2021-01-12 04:00:00', '2021-01-12 04:30:00', '2021-01-12 05:00:00', '2021-01-12 05:30:00', '2021-01-12 06:00:00', '2021-01-12 06:30:00', '2021-01-12 07:00:00', '2021-01-12 07:30:00', '2021-01-12 08:00:00', '2021-01-12 08:30:00', '2021-01-12 09:00:00', '2021-01-12 09:30:00', '2021-01-12 10:00:00', '2021-01-12 10:30:00', '2021-01-12 11:00:00', '2021-01-12 11:30:00', '2021-01-12 12:00:00', '2021-01-12 12:30:00', '2021-01-12 13:00:00', '2021-01-12 13:30:00', '2021-01-12 14:00:00', '2021-01-12 14:30:00', '2021-01-12 15:00:00', '2021-01-12 15:30:00', '2021-01-12 16:00:00', '2021-01-12 16:30:00', '2021-01-12 17:00:00', '2021-01-12 17:30:00', '2021-01-12 18:00:00', '2021-01-12 18:30:00', '2021-01-12 19:00:00', '2021-01-12 19:30:00', '2021-01-12 20:00:00', '2021-01-12 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'21.99', '21.84', '21.67', '21.47', '15.15', '21.06', '21.01', '22.07', '22.44', '22.44', '22.37', '22.21', '22.0', '21.71', '21.46', '21.2', '20.94', '20.72', '20.46', '20.23', '20.06', '19.92', '19.71', '19.64', '20.06', '21.13', '21.2', '21.51', '22.65', '22.22', '21.4', '20.9', '20.81', '20.92', '20.91', '20.85', '20.67', '20.64', '21.13', '21.3', '21.56', '23.09', '23.84', '23.9', '23.72', '23.42', '23.09', '22.73', '22.43', '22.11', '21.75', '21.45', '21.08', '20.75', '20.43', '20.12', '19.84', '19.52', '19.26', '18.92', '18.67', '18.34', '18.08', '17.82', '17.54', '17.35', '17.12', '16.84', '16.63', '16.46', '16.27', '16.35', '17.27', '18.71', '20.0', '21.28', '22.58', '24.02', '25.12', '25.23', '24.17', '23.85', '23.74', '24.41', '24.5', '24.38', '23.7', '21.28', '20.75', '21.94', '23.12', '24.92', '25.31', '25.22', '25.04', '24.79', '24.51', '24.19', '23.84', '23.53', '23.19', '22.85', '22.48', '22.1', '21.78', '21.41', '21.06', '20.75', '20.43', '20.15', '19.86', '19.56', '19.27', '19.0', '18.77', '18.46', '18.2', '18.05', '18.06', '18.2', '18.86', '20.03', '21.27', '22.5', '22.6', '21.81', '22.05', '22.62', '22.86', '23.07', '23.28', '23.32', '23.54', '23.53', '23.02', '22.38', '21.67', '20.96', '20.38', '19.84', '19.38', '19.49', '20.21', '20.49', '20.67', '20.83', '21.01', '21.15', '21.32', '21.46', '21.55', '21.66', '21.76', '21.88', '21.99', '22.09', '22.23', '22.31', '22.48', '22.66', '22.79', '22.94', '23.06', '23.23', '23.35', '23.55', '23.87', '24.14', '24.5', '24.84', '25.11', '25.33', '25.66', '26.04', '26.33', '26.55', '26.8', '27.14', '27.3', '27.43', '27.58', '27.67', '27.51', '27.48', '27.49', '27.51', '27.84', '28.21', '28.55', '28.71', '28.9', '28.92', '29.08', '29.16', '29.18', '29.28', '29.29', '29.3', '29.31', '29.31', '29.34', '29.34', '29.34', '29.31', '29.36', '29.37', '29.33', '29.34', '29.33', '29.34', '29.34', '29.37', '29.46', '29.48', '29.5', '29.64', '29.88', '30.13', '30.12', '30.32', '30.53', '30.63', '30.76', '30.92', '31.26', '31.52', '31.24', '31.24', '31.11', '30.98', '30.8', '30.99', '33.04', '34.59', '35.13', '35.3', '35.4', '35.32', '35.21', '35.05', '34.87', '34.68', '34.53', '34.32', '34.14', '33.98', '33.84', '33.68', '33.55', '33.35', '33.2', '33.02', '32.84', '32.63', '32.42', '32.12', '31.84', '31.53', '31.21', '30.89', '30.58', '30.31', '30.12', '30.11', '31.04', '32.15', '33.24', '34.18', '35.36', '36.4', '37.34', '37.64', '37.47', '37.42', '37.7', '37.56', '37.06', '36.69', '36.28', '35.86', '34.46', '33.65', '32.9', '32.19', '31.72', '30.82', '29.9', '29.19', '28.52', '27.93', '27.48', '26.82', '26.22', '25.38', '24.79', '25.33', '25.96', '26.24', '26.42', '26.57', '26.77', '26.99', '27.16', '27.31', '27.51', '27.64', '27.89', '28.26', '28.66', '29.02', '29.36', '29.69', '29.97', '30.4', '30.78', '31.23', '31.88', '32.19', '32.54', '32.73', '32.97', '33.11', '33.09', '33.21', '33.26', '33.2', '33.08', '32.99', '32.94', '32.73', '32.57', '32.37', '32.21', '31.99', '31.74', '31.69', '31.58', '31.48', '31.42', '31.33', '31.24', '31.12', '31.06', '30.92', '30.81', '30.72', '30.6', '30.5', '30.36', '30.21', '30.09', '30.01', '29.92', '29.85', '29.8', '29.68', '29.62', '29.52', '29.47', '29.49', '29.63', '30.21', '30.13', '30.19', '30.1', '30.19', '30.25', '30.17', '29.95', '29.75', '29.6', '29.59', '29.24', '29.0', '28.69', '28.16', '27.51', '27.44', '28.04', '28.11', '27.98', '27.71', '27.56', '27.31', '26.97', '26.75', '26.61', '26.59', '26.74', '26.79', '26.82', '26.81', '26.8', '26.8', '26.75', '26.62', '26.51', '26.32', '26.13', '25.93', '25.78', '25.59', '25.43', '25.32', '25.25', '25.23', '25.24', '25.35', '25.64', '25.98', '26.25', '26.72', '27.15', '27.54', '27.98', '28.23', '28.47', '28.68', '28.87', '28.99', '29.82', '32.67', '33.68', '33.97', '33.98', '33.84', '33.62', '33.38', '33.07', '32.69', '32.35', '31.99', '31.68', '31.4', '31.14', '30.9', '30.61', '30.32', '30.02', '29.65', '29.29', '28.92', '28.56', '28.16', '27.77', '27.42', '27.06', '26.72', '26.36', '26.03', '25.69', '25.41', '25.09', '24.86', '24.78', '24.83', '25.17', '26.1', '25.64', '26.1', '27.52', '27.98', '27.86', '27.62', '27.33', '27.31', '27.03', '26.96', '26.73', '26.34', '25.89', '25.58', '24.91', '24.07', '23.18', '22.29', '22.12', '23.32', '23.76', '23.98', '24.07', '24.09', '24.11', '24.13', '24.12', '24.11', '24.06', '24.06', '24.03', '24.04', '23.98', '23.91', '23.98', '24.06', '24.06', '24.09', '24.1', '24.16', '24.22', '24.25', '24.35', '24.43', '24.5', '24.58', '24.71', '25.14', '25.53', '25.78', '26.1', '26.52', '26.9', '27.73', '28.67', '29.23', '29.32', '29.31', '29.37', '29.33', '29.63', '29.46', '29.03', '28.34', '27.48', '27.57', '27.5', '26.97', '26.38', '25.8', '25.46', '25.17', '24.89', '24.76', '25.25', '26.62', '27.55', '27.8', '27.79', '27.68', '27.62', '27.41', '27.28', '27.13', '26.92', '26.67', '26.44', '26.18', '25.98', '25.88', '25.76', '25.64', '25.55', '25.59', '25.7', '25.85', '26.15', '26.71', '28.2', '28.02', '28.1', '29.3', '29.69', '29.34', '29.36', '29.31', '29.14', '29.1', '28.99', '29.32', '29.97', '30.01', '29.81', '29.57', '29.37', '29.11', '28.83', '28.65', '28.36', '28.03', '27.74', '27.41', '27.12', '26.71', '26.36', '26.04', '25.71', '25.43', '25.22', '25.04', '24.83', '24.63', '24.51', '24.33', '24.22', '24.09', '23.99', '23.89', '23.85', '23.75', '23.84', '23.92', '24.25', '24.58', '25.54', '26.51', '26.65', '27.22', '27.75', '28.05', '28.31', '27.93', '27.6', '28.38', '28.41', '28.33', '28.11', '27.89', '26.74', '25.28', '24.15', '23.65', '22.66', '21.16', '19.72', '18.55', '17.5', '16.72', '18.13', '18.87', '19.23', '19.37', '19.44', '19.44', '19.41', '19.24', '19.03', '18.85', '18.65', '18.41', '18.13', '17.87', '17.57', '17.26', '17.04', '16.79', '16.6', '16.42', '16.46', '16.67', '17.12', '17.87', '19.26', '20.05', '20.52', '20.86', '21.02', '19.49', '18.14', '17.78', '17.38', '17.05', '18.02', '19.08', '19.42', '19.6', '18.79', '17.92', '16.6', '15.25', '13.72', '13.17', '15.02', '15.55', '15.76', '15.82', '15.82', '15.81', '9.97', '15.52', '15.3', '15.09', '14.84', '14.6', '14.31', '14.07', '13.78', '13.5', '13.24', '12.98', '12.81', '12.6', '12.36', '12.24', '12.03', '11.95', '12.12', '12.4', '12.89', '13.37', '13.88', '14.36', '14.81', '14.84', '15.14', '15.21', '15.99', '16.91', '17.65', '18.13', '18.35', '18.43', '17.67', '16.82', '16.3', '15.89', '16.03', '15.79', '15.41', '15.01', '14.68', '14.46', '14.09', '13.81', '13.68', '13.71', '14.55', '15.02', '15.37', '15.68', '15.94', '16.19', '16.41', '16.62', '16.76', '16.95', '17.12', '17.25', '17.41', '17.56', '17.68', '17.79', '17.95', '18.07', '18.23', '18.48', '18.88', '19.9', '21.47', '22.58', '23.64', '24.18', '24.99', '26.21', '27.27', '27.85', '28.29', '27.92', '27.63', '27.38', '27.21', '26.95', '26.71', '26.43', '26.04', '25.64', '25.15', '24.21', '23.11', '22.07', '21.56', '22.29', '22.34', '22.16', '21.97', '21.78', '21.55', '21.4', '21.37', '21.33', '21.35', '21.22', '21.14', '21.17', '21.18', '21.2', '21.21', '21.21', '21.22', '21.3', '21.41', '21.61', '21.96', '22.26', '22.6', '22.94', '23.26', '23.67', '24.12', '24.56', '24.92', '25.17', '25.61', '25.87', '26.19', '26.5', '26.33', '26.25', '26.15', '26.02', '25.87', '25.68', '25.47', '25.33', '25.18', '25.02', '24.89', '24.67', '24.53', '24.34', '24.2', '24.0', '23.75', '23.46', '23.53', '23.84', '23.97', '24.08', '24.1', '24.23', '24.27', '24.33', '24.4', '24.43', '24.46', '24.55', '24.62', '24.63', '24.68', '24.78', '24.87', '25.05', '25.32', '25.69', '26.0', '26.19', '26.41', '26.61', '26.74', '26.65', '26.55', '26.77', '26.8', '26.7', '26.52', '25.25', '23.15', '23.58', '23.91', '23.81', '23.65', '23.06', '24.73', '25.07', '24.94', '24.63', '24.37', '24.02', '23.7', '23.35', '23.01', '22.71', '22.44', '22.18', '21.83', '21.32', '20.8', '20.25', '19.74', '19.2', '18.76', '18.36', '17.77', '17.3', '16.87', '16.47', '16.12', '16.22', '16.46', '16.91', '17.88', '19.23', '20.41', '21.64', '22.53', '22.18', '22.79', '23.32', '24.08', '26.07', '26.96', '26.61', '26.36', '26.25', '26.12', '25.85', '25.66', '25.5', '24.93', '24.63', '24.25', '23.57', '23.29', '22.7', '22.17', '21.73', '15.43', '21.39', '22.25', '22.57', '22.78', '22.84', '22.94', '22.94', '22.89', '22.83', '22.81', '22.75', '22.66', '22.54', '22.42', '22.27', '22.11', '21.94', '21.81', '21.78', '21.9', '22.14', '22.58', '23.01', '23.32', '23.71', '24.13', '24.55', '25.31', '26.17', '26.31', '26.51', '26.76', '27.1', '27.18', '27.2', '27.23', '27.17', '27.18', '27.18', '27.09', '26.59', '26.18', '25.82', '25.46', '25.05', '24.74', '24.42', '24.06', '23.79', '24.14', '24.55', '24.76', '24.85', '24.91', '24.96', '24.98', '24.99', '25.06', '25.09', '25.08', '25.05', '25.0', '24.98', '24.96', '24.89', '24.86', '24.89', '24.97', '25.06', '25.27', '25.54', '25.88', '26.3', '26.71', '27.11', '27.33', '27.43', '27.59', '27.69', '27.78', '27.8', '27.62', '27.13', '26.72', '26.45', '26.31', '26.69', '26.93', '27.02', '27.48', '27.98', '27.87', 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'1.07', '1.03', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '9.24', '0.0', '0.0', '1.03', '3.0', '5.17', '1.0', '1.0', '2.03', '6.1', '9.21', '2.11', '1.03', '4.03', '3.0', '0.0', '7.1', '3.1', '6.07', '0.0', '1.03', '1.0', '2.07', '2.03', '4.07', '3.03', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '1.03', '0.0', '0.0', '0.0', '0.0', '2.0', '5.03', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '1.0', '4.07', '2.0', '0.0', '4.07', '10.17', '1.03', '0.0', '1.03', '7.17', '0.0', '17.28', '6.03', '4.07', '2.03', '8.14', '2.03', '4.21', '1.0', '5.03', '3.03', '5.07', '3.07', '0.0', '2.03', '2.03', '0.0', '1.07', '5.14', '0.0', '2.03', '0.0', '0.0', '2.03', '0.0', '2.0', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '1.0', '1.0', '0.0', '0.0', '0.0', '1.0', '6.32', '0.0', '1.0', '0.0', '1.0', '1.03', '7.07', '2.07', '1.0', '4.07', '8.46', '2.0', '9.17', '10.21', '11.14', '7.1', '13.17', '9.28', '6.07', '3.03', '3.1', '4.21', '5.1', '5.1', '6.14', '5.1', '3.07', '0.0', '1.0', '0.0', '0.0', '3.18', '1.03', '1.0', '1.0', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '1.0', '0.0', '1.03', '0.0', '0.0', '7.17', '0.0', '0.0', '2.03', '5.21', '3.07', '2.0', '4.07', '8.14', '2.03', '8.14', '9.21', '25.45', '15.34', '13.68', '18.52', '20.31', '31.52', '15.24', '10.28', '11.17', '11.14', '9.07', '10.21', '5.21', '2.0', '3.03', '0.0', '0.0', '3.07', '1.03', '1.0', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '1.0', '0.0', '0.0', '1.07', '0.0', '0.0', '7.14', '0.0', '1.0', '5.1', '2.0', '5.14', '5.14', '6.17', '1.07', '0.0', '11.14', '4.1', '5.0', '7.21', '9.14', '9.17', '11.21', '3.0', '3.14', '3.07', '3.03', '4.03', '6.07', '4.1', '3.07', '3.07', '1.0', '4.21', '2.03', '1.03', '1.0', '1.0', '3.0', '2.0', '4.07', '3.03', '0.0', '1.04', '0.0', '1.0', '0.0', '1.03', '0.0', '0.0', '0.0', '1.0', '1.0', '5.1', '3.21', '0.0', '0.0', '4.0', '1.03', '3.0', '1.0', '1.0', '3.03', '6.07', '6.18', '3.21', '9.17', '9.24', '14.24', '10.21', '11.1', '7.07', '19.34', '8.07', '2.07', '6.14', '6.1', '3.03', '1.0', '6.07', '1.03', '2.0', '2.03', '2.07', '3.03', '1.07', '4.07', '0.0', '1.03', '0.0', '0.0', '1.0', '0.0', '0.0', '0.0', '1.04', '1.03', '0.0', '0.0', '0.0', '1.0', '0.0', '8.14', '0.0', '0.0', '2.07', '0.0', '0.0', '3.03', '3.03', '2.03', '0.0', '5.03', '9.21', '7.1', '10.24', '7.43', '6.1', '6.17', '4.03', '4.07', '7.07', '4.1', '1.0', '0.0', '0.0', '1.04', '1.03', '3.03', '0.0', '0.0', '1.03', '4.14', '0.0', '2.03', '1.03', '0.0', '5.07', '0.0', '0.0', '0.0', '1.03', '0.0', '0.0', '0.0', '0.0', '0.0', '4.25', '0.0', '0.0', '0.0', '0.0', '0.0', '2.03', '2.07', '5.07', '0.0', '0.0', '1.03', '0.0', '0.0', '4.1', '0.0', '3.1', '2.03', '2.0', '1.03', '0.0', '2.07', '0.0', '1.03', '3.03', '0.0', '0.0', '0.0', '1.0', '0.0', '1.03', '0.0', '1.0', '0.0', '1.03', '1.03', '2.03', '0.0', '0.0', '0.0', '0.0', '0.0', '2.0', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '2.03', '2.03', '0.0', '0.0', '1.0', '2.03', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '4.25', '3.07', '5.1', '5.1', '5.1', '8.21', '1.03', '4.1', '2.0', '4.1', '4.21', '1.0', '0.0', '2.0', '2.07', '1.03', '2.03', '3.07', '1.0', '1.03', '2.07', '0.0', '1.03', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '4.03', '0.0', '0.0', '0.0', '0.0', '5.14', '7.1', '1.03', '5.1', '10.17', '14.24', '6.07', '1.07', '3.03', '1.03', '5.1', '4.07', '8.07', '4.07', '4.07', '2.07', '5.07', '4.07', '1.04', '1.03', '2.07', '0.0', '3.03', '1.03', '0.0', '1.0', '1.0', '1.0', '3.14', '1.03', '0.0', '1.03', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '2.0', '2.07', '1.03', '0.0', '1.03', '0.0', '0.0', '4.03', '1.03', '6.1', '0.0', '11.17', '8.21', '10.1', '7.1', '1.0', '10.17', '7.07', '1.03', '2.03', '5.25', '4.03', '4.1', '4.07', '10.17', '4.07', '1.0', '3.72', '6.55', '1.03', '1.07', '0.0', '0.0', '2.07', '2.03', '0.0', '0.0', '0.0', '0.0', '1.03', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '2.03', '0.0', '0.0', '0.0', '2.0', '1.03', '1.03', '2.03', '3.03', '3.07', '3.1', '3.07', '5.14', '9.1', '9.54', '19.38', '7.14', '8.14', '4.07', '6.14', '4.14', '2.07', '7.17', '5.03', '3.21', '1.0', '4.07', '6.14', '1.03', '0.0', '1.03', '1.03', '2.03', '2.03', '0.0', '0.0', '0.0', '0.0', '0.0', '0.0', '1.03', '1.03', '0.0', '0.0', '1.07', '0.0', '1.03', '0.0', '1.03', '0.0', '0.0', '1.03', '0.0', '0.0', '0.0', '6.36', '5.1', '2.07', '4.1', '4.07', '6.14', '0.0', '6.17', '7.14', '8.17', '10.54', '0.0', '3.1', '4.1', '4.14', '10.17', '7.1', '2.03', '4.1', '2.07', '6.32', '3.03', '1.03', '3.07', '1.03', '4.03', '0.0', '1.0', '0.0', '0.0', '0.0', '1.04', '1.03', '0.0', '0.0', '0.0', '0.0', '2.07', '1.03', '0.0', '0.0', '3.18', '0.0', '0.0', '0.0', '1.03', '0.0', '0.0', '0.0', '3.1', '4.1', '5.1', '4.25', '2.03', '4.03', '10.14', '5.07', '8.24', '8.17', '12.24', '2.03', '3.07', '1.04', '6.07', '0.0', '1.03', '2.03', '1.03', '3.03', '4.03', '1.0', '0.0', '0.0', '11.17', '3.07', '1.03', '0.0', '0.0', '0.0', '0.0', '0.0']}

Q1-draw a scatter plot of outside temperature over the entire year

values_temp_8 = df['temp_8']
temp_8_q1 = [float(value) for value in values_temp_8]

plt.figure(figsize=(10, 6))
plt.scatter(df["pitime"], temp_8_q1, color='red', label='Outside Temperature Over the Year')
# Adding labels and title
plt.xlabel('pitime')
plt.ylabel('Temperature (°F)')
plt.title('Outside Temperature Over the Year')
# Display the legend
plt.legend()
# Display the plot
plt.show()

Q2-draw a histogram of temperature binned into 50 bins over the whole year

selected_column = 'temp_8'
values = df['temp_8']
plt.figure(figsize=(10, 6))
plt.hist(values, bins=50, alpha=0.7, label=selected_column)
plt.title('Temperature Histogram Over the Year')
plt.xlabel('Temperature (°F)')
plt.ylabel('Frequency')
plt.show()

Q3-draw a scatter plot of 'adc_67_avg' as x and 'adc_68_avg' as y, with an x range of 512-518

values_adc67 = df['adc_67_avg']
adc_67_avg_df = [float(value) for value in values_adc67]

values_adc68 = df['adc_68_avg']
adc_68_avg_df = [float(value) for value in values_adc68]

x_data = adc_67_avg_df[512:518]
y_data = adc_68_avg_df[512:518]

print(x_data)
print(y_data)

[516.57, 516.06, 516.12, 516.03, 516.07, 516.0]
[503.91, 503.94, 503.88, 503.9, 503.96, 503.9]

plt.figure(figsize=(10, 6))
plt.scatter(x_data, y_data, color='red')
# Adding labels and title
plt.xlabel('adc_67_avg')
plt.ylabel('adc_68_avg')
plt.title('Scatter Plot of adc_67_avg vs adc_68_avg')
# Display the plot
plt.show()

Q4-what might cause the the stepping pattern in the relationship?

In the above graph there is not a perticuler pattern between liquid moisture basement floor east average and liquid moisture basement floor west average. The above scatter plot does not show any concrete relationshipt b/w avvgs of liquid moisture basement floor east and liquid moisture basement floor west.

Q5-draw a histogram of the outdoor relative humidity.

values_rh_3 = df['rh_3']
rh_3_q5 = [float(value) for value in values_rh_3]

plt.figure(figsize=(10, 6))
plt.hist(rh_3_q5, bins=50, alpha=0.7, label=selected_column)
plt.title('Histogram of Outdoor Relative Humidity')
plt.xlabel('Relative Humidity (%)')
plt.ylabel('requency')
plt.show()

Q6- is the distribution normal? what might bias this measurement?

Ans the distributin is not normal as The majority of data points are concentrated on the left side, with a long tail extending to the right. The mean is typically greater than the median, and the mode is less than the median. A histogram skewed to the right means that the peak of the graph lies to the left side of the center. On the right side of the graph, the frequencies of observations are lower than the frequencies of observations to the left side.

Q7-draw a scatter plot of rh_4 to co2 colored by temp_8. Use the 'summer' cmap

values_rh_4 = df['rh_4']
values_rh_4_q7 = [float(value) for value in values_rh_4]

values_co2 = df['co2']
values_co2_q7 = [float(value) for value in values_co2]

values_temp_8 = df["temp_8"]
temp_8_df = [float(value) for value in values_temp_8]

plt.figure(figsize=(10, 6))
plt.scatter(values_rh_4_q7, values_co2_q7, c = temp_8_df, cmap='summer', edgecolors='black', alpha=0.7)
#c=df["temp_8"]
# Adding labels and title
plt.xlabel('rh_4')
plt.ylabel('co2')
plt.colorbar(label='temp_8')
plt.title('Scatter Plot of rh_4 vs co2 (Colored by temp_8)')
# Display the plot
plt.show()

Q8-draw a scatter plot of 'motion front yard right' and 'motion front yard left'. use outside temperature as you color

values_trpin_40_sampleson = df['trpin_40_sampleson']
values_values_trpin_40_sampleson_q8 = [float(value) for value in values_trpin_40_sampleson]

values_trpin_44_sampleson = df['trpin_44_sampleson']
values_trpin_44_sampleson_q8 = [float(value) for value in values_trpin_44_sampleson]

values_temp_8 = df["temp_8"]
temp_8_df = [float(value) for value in values_temp_8]

plt.figure(figsize=(10, 6))
plt.scatter(values_values_trpin_40_sampleson_q8, values_trpin_44_sampleson_q8,c= temp_8_df, cmap='summer', edgecolors='black', alpha=0.7)
# Adding labels and title
plt.xlabel('Motion Front Yard Right')
plt.ylabel('Motion Front Yard Left')
plt.colorbar(label='Outside Air Temperature')
plt.title('Scatter Plot of Motion Front Yard Right vs Motion Front Yard Left (Colored by Outside Temperature)')
# Display the plot
plt.show()

Q9-what does the color tell us about the a bias in the data?

When the Outside Temperature increases Motion Front Yard Left side trigger pins produces most of the data as based on motion sensor. As clearly we can se in the above scatter plot.